2015-08-02T18:26:07ZAsistente Robótico Social Interactivo para Terapias de Rehabilitación Motriz con Pacientes de Pediatríahttp://hdl.handle.net/10016/20483
Asistente Robótico Social Interactivo para Terapias de Rehabilitación Motriz con Pacientes de Pediatría
Calderita, L. V.; Bustos, P.; Fernández, Fernando; Viciana, R.; Bandera, A.; Suárez Mejías
El objetivo de las terapias de rehabilitación motriz es la recuperación de zonas dañadas mediante la repetición de ciertas actividades motrices. En este esquema, la recuperación del paciente depende directamente de su adherencia al tratamiento, por lo que las terapias convencionales, con sus intensivas sesiones de rehabilitación que se prolongan en el tiempo, provocan en numerosas ocasiones su desmotivación, haciendo que no se consiga siempre que éste cumpla con los objetivos fijados. Por otra parte, la correcta ejecución de estas terapias en hospitales y otros centros médicos requieren una dedicación y esfuerzo importante y continuado por parte de los profesionales médicos, lo que supone a su vez un coste importante para las instituciones sanitarias. En este ámbito de aplicación, este artículo describe el desarrollo de una terapia de rehabilitación motriz novedosa, centrada en un robot socialmente interactivo, que se convierte en fuente de motivación pero también en un asistente para llevar a cabo terapias rehabilitadoras personalizadas. La experiencia ha sido también el germen del diseño e implementación de una arquitectura de control novedosa, RoboCog, que ha dotado al robot de las capacidades perceptivas y cognitivas que le permiten exhibir un comportamiento socialmente desarrollado y pro-activo. Las pruebas de verificación llevadas a cabo sobre los distintos elementos de la arquitectura muestran el correcto funcionamiento de éstos y de su integración con el resto de la arquitectura. Además, dicha terapia ha sido evaluada satisfactoriamente en sesiones individuales con pacientes de pediatría con parálisis braquial obstétrica (PBO), una patología producida por un daño adquirido en el momento del nacimiento y que afecta a la movilidad motriz de las extremidades superiores, pero no a sus capacidades intelectuales y comunicativas.; Motor rehabilitation therapy pursuits the recovery of damaged areas from the repetitive practice of certain motor activities. The patient's recovery directly depends on the adherence to rehabilitation therapy. Conventional methods consisting of repetitions usually make the patient feel unmotivated and neglect complying with the appropriate treatments. In addition, the treatment of these motor deficits requires intensive and extended rehabilitation sessions that demand sustained dedication and effort by professionals and incur in accretive costs for the institutions. Within this framework, this paper describes the development and evaluation of a new neurorehabilitation therapy, whose core is a socially interactive robot. This robot is able to consistently engaged patients in the therapeutic interaction, providing tireless motivation, encouragement and guidance. The experience has also been the origin of the design and implementation of a novel control architecture, RoboCog, which has provided the robot perceptual and cognitive capabilities that allow a behavior more socially developed, proactive. Verification tests carried out on the various components of the architecture show us the proper working of these and its integration with the rest of the architecture. Furthermore, this therapy has been successfully with congenital brachial palsy (PBO), a disease caused by damage acquired at birth and affects motor mobility of the upper limbs, but not their intellectual and communicative abilities.
2015-03-01T00:00:00ZAutomatic Compilation of Objects to Counters in Automatic Planning. Case of study: Creation Planninghttp://hdl.handle.net/10016/19707
Automatic Compilation of Objects to Counters in Automatic Planning. Case of study: Creation Planning
Rosa Turbides, Tomás Eduardo de la; Fuentetaja, Raquel
In classical planning, all objects should be represented as constants explicitly, even though their names could be irrelevant, which produces severe instantiation problems. This is specially problematic in tasks with actions for creating new objects, as it involves to estimate how many potential new objects will be necessary to solve
the task. We propose a new automatic compilation from the classical to a numeric planning model to represent objects with irrelevant names using numerical functions. The compilation reduces the size of the instantiation and avoids the need of estimating the number of future objects in advance. The compiled planning task can be solved several orders of magnitude faster than its equivalent classical model.
2014-11-17T00:00:00ZLearning Teaching Strategies in an Adaptive and Intelligent Educational System through Reinforcement Learninghttp://hdl.handle.net/10016/17287
Learning Teaching Strategies in an Adaptive and Intelligent Educational System through Reinforcement Learning
Iglesias, Ana; Martínez, Paloma; Aler, Ricardo; Fernández, Fernando
One of the most important issues in Adaptive and Intelligent Educational Systems (AIES) is to define effective pedagogical policies for tutoring students according to their needs. This paper proposes to use Reinforcement Learning (RL) in the pedagogical module of an educational system so that the system learns automatically which is the best pedagogical policy for teaching students. One of the main characteristics of this approach is its ability to improve the pedagogical policy based only on acquired experience with other students with similar learning characteristics. In this paper we study the learning performance of the educational system through three important issues. Firstly, the learning convergence towards accurate pedagogical policies. Secondly, the role of exploration/exploitation strategies in the application of RL to AIES. Finally, a method for reducing the training phase of the AIES.
2009-08-01T00:00:00ZAn experience applying reinforcement learning in a web-based adaptive and intelligent educational systemhttp://hdl.handle.net/10016/16247
An experience applying reinforcement learning in a web-based adaptive and intelligent educational system
Iglesias, Ana; Martínez, Paloma; Fernández, Fernando
The definition of effective pedagogical strategies for coaching and tutoring students according to their needs is one of the most important issues in Adaptive and Intelligent Educational Systems (AIES). The use of a Reinforcement Learning (RL) model allows the system to learn automatically how to teach to each student individually, only based on the acquired experience with other learners with similar characteristics, like a human tutor does. The application of this artificial intelligence technique, RL, avoids to define the teaching strategies by learning action policies that define what, when and how to teach. In this paper we study the performance of the RL model in a DataBase Design (DBD) AIES, where this performance is measured on number of students required to acquire efficient teaching strategies.
2003-03-01T00:00:00Z